[61] J Sirotković, H Dujmic, and V Papić. Accelerating mean shift image segmentation with ifgt on massively parallelgpu. In International Convention on Information and Communication Technology, Electronics and Microelectronics, 2013. 2
[62] Vincent Sitzmann, Julien Martel, Alexander Bergman, David Lindell, and Gordon Wetzstein. Implicit neural representations with periodic activation functions. In NeurIPS, 2020. 2
[63] Michael J Smanski, Swapnil Bhatia, Dehua Zhao, YongJin Park, Lauren BA Woodruff, Georgia Giannoukos, Dawn Ciulla, Michele Busby, Johnathan Calderon, Robert Nicol, et al. Functional optimization of gene clusters by combinatorial design and assembly. Nature biotechnology, 32(12):1241-1249, 2014. 3
[64] Douglas R Smith. The design of divide and conquer algorithms. Science of Computer Programming, 5:37-58, 1985. 2
[65] Behzad Tabibian, Utkarsh Upadhyay, Abir De, Ali Zarezade, Bernhard Scholkopf, and Manuel Gomez-Rodriguez. Enhancing human learning via spaced repetition optimization. Proceedings of the National Academy of Sciences, 116(10):3988-3993, 2019. 3
[66] David Tarditi, Sidd Puri, and Jose Oglesby. Accelerator: using data parallelism to program gpus for general-purpose uses. ACM SIGPLAN Notices, 41(11):325-335, 2006. 2
[67] Pei Wang, Kabir Nagrecha, and Nuno Vasconcelos. Gradient-based algorithms for machine teaching. In CVPR, 2021. 1, 3
[68] Pei Wang and Nuno Vasconcelos. A machine teaching framework for scalable recognition. In ICCV, 2021. 1
[69] Zhaozhuo Xu, Beidi Chen, Chaojian Li, Weiyang Liu, Le Song, Yingyan Lin, and Anshumali Shrivastava. Locality sensitive teaching. In NeurIPS, 2021. 3
[70] Jianbo Ye, Panruo Wu, James Z Wang, and Jia Li. Fast discrete distribution clustering using Wasserstein barycenter with sparse support. IEEE Transactions on Signal Processing, 65(9):2317-2332, 2017. 3
[71] Teresa Yeo, Parameswaran Kamalaruban, Adish Singla, Arpit Merchant, Thibault Asselborn, Louis Faucon, Pierre Dillenbourg, and Volkan Cevher. Iterative classroom teaching. In AAAI, 2019. 3
[72] Longhui Yu, Weisen Jiang, Han Shi, Jincheng Yu, Zhengying Liu, Yu Zhang, James T Kwok, Zhenguo Li, Adrian Weller, and Weiyang Liu. Metamath: Bootstrap your own mathematical questions for large language models. arXiv preprint arXiv:2309.12284, 2023. 10
[73] Chen Zhang, Xiaofeng Cao, Weiyang Liu, Ivor Tsang, and James Kwok. Nonparametric iterative machine teaching. In ICML, 2023. 1, 2, 3, 4, 5, 6, 7, 9, 23
[74] Chen Zhang, Guangyu Sun, Zhenman Fang, Peipei Zhou, Peichen Pan, and Jason Cong. Caffeine: Toward uniformed representation and acceleration for deep convolutional neural networks. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 38(11):2072-2085, 2018. 2
[75] Dinghuai Zhang, Mao Ye, Chengyue Gong, Zhanxing Zhu, and Qiang Liu. Black-box certification with randomized smoothing: A functional optimization based framework. In NeurlPS, 2020. 3
[76] Xuezhou Zhang, Shubham Kumar Bharti, Yuzhe Ma, Adish Singla, and Xiaojin Zhu. The sample complexity of teaching-by-reinforcement on q-learning. arXiv preprint arXiv:2006.09324, 2020. 3
[77] Yu Zhang and Qiang Yang. A survey on multi-task learning. IEEE Transactions on Knowledge and Data Engineering, 34(12):5586-5609, 2021. 2, 8
[78] Yao Zhou, Arun Reddy Nelakurthi, and Jingrui He. Unlearn what you have learned: Adaptive crowd teaching with exponentially decayed memory learners. In SIGKDD, 2018. 1
[79] Yao Zhou, Arun Reddy Nelakurthi, Ross Maciejewski, Wei Fan, and Jingrui He. Crowd teaching with imperfect labels. In The Web Conference, 2020. 1
[80] Xiaojin Zhu. Machine teaching for bayesian learners in the exponential family. arXiv preprint arXiv:1306.4947, 2013. 1
[81] Xiaojin Zhu. Machine teaching: An inverse problem to machine learning and an approach toward optimal education. In AAAI, 2015. 1, 3
[82] Xiaojin Zhu, Ji Liu, and Manuel Lopes. No learner left behind: On the complexity of teaching multiple learners simultaneously. In *IJCAI*, 2017. 3